Training course

Overview

Industrial Engineering is a comprehensive professional training course designed to develop the technical, analytical, and problem-solving capabilities required to improve productivity, efficiency, quality, safety, and cost performance across industrial and service operations. The course introduces participants to the systematic design, analysis, and optimization of integrated systems involving people, materials, equipment, information, processes, and resources. It provides a practical foundation for professionals seeking to improve operational performance, eliminate waste, optimize workflows, and make evidence-based engineering and management decisions.

This industrial engineering training covers core methodologies and tools used to analyze and improve operational systems, including work study, method study, time study, process mapping, value stream mapping, facility layout, capacity planning, production planning, inventory management, ergonomics, quality improvement, and operations research. Participants will explore Lean principles, Six Sigma, Kaizen, Theory of Constraints, PDCA, DMAIC, and other established improvement frameworks. Practical analytical techniques such as Pareto analysis, cause-and-effect diagrams, productivity measurement, line balancing, takt time, standard work, process capability, and basic statistical analysis are integrated throughout the program.

The course emphasizes practical application through workplace exercises, engineering calculations, case studies, process improvement scenarios, production-system simulations, and operational problem-solving activities. Participants will learn how to identify bottlenecks, analyze process losses, measure labor and equipment utilization, improve workplace layouts, balance production lines, optimize resource allocation, and evaluate alternative operating methods. The program also examines how industrial engineering principles can support quality, safety, maintenance, supply chain performance, sustainability, digital transformation, and continuous improvement.

By the end of this five-day industrial engineering course, participants will be able to apply structured industrial engineering methods to analyze existing systems, identify improvement opportunities, design more efficient processes, and support sustainable operational performance. The course progresses from fundamental industrial engineering concepts to advanced productivity analysis, optimization, quality improvement, systems thinking, and digital industrial engineering. A final practical case study and improvement project enables participants to integrate the tools and techniques covered throughout the training into a realistic industrial engineering improvement solution.

Course Duration

5 Days (40 Hours)

Target Participants

·         Industrial Engineers and Engineering Professionals

·         Production and Manufacturing Engineers

·         Operations and Process Improvement Professionals

·         Production Managers and Supervisors

·         Operations Managers and Plant Managers

·         Supply Chain and Logistics Professionals

·         Quality and Continuous Improvement Professionals

·         Maintenance and Reliability Professionals

·         Project Managers involved in operational improvement

·         Professionals responsible for productivity, efficiency, cost reduction, and process optimization

Course Objectives

By the end of the training, participants will be able to:

·         Explain the principles, scope, and applications of industrial engineering

·         Analyze production and service systems using systematic engineering approaches

·         Map, measure, and evaluate operational processes and workflows

·         Conduct work studies, method studies, and time studies to improve productivity

·         Calculate productivity, utilization, efficiency, capacity, takt time, and other operational measures

·         Identify bottlenecks, constraints, waste, delays, and process inefficiencies

·         Apply Lean, Six Sigma, Kaizen, PDCA, DMAIC, and Theory of Constraints principles

·         Design improved facility layouts, workflows, workstations, and production lines

·         Apply ergonomics and human factors principles to improve workplace performance and safety

·         Analyze inventory, material flow, capacity, scheduling, and resource allocation problems

·         Use statistical and quantitative techniques to support industrial engineering decisions

·         Apply quality improvement tools to reduce defects, variation, and process losses

·         Evaluate operational costs and identify opportunities for sustainable cost reduction

·         Apply simulation, optimization, and data-driven methods to complex operational problems

·         Develop practical industrial engineering improvement projects and implementation plans

Course Content

Day 1: Industrial Engineering Foundations, Systems Thinking, and Process Analysis

Module 1: Industrial Engineering Foundations, Systems Thinking, and Process Analysis

1.      Introduction to Industrial Engineering, Scope, Roles, and Professional Applications

2.      Industrial Engineering Systems: People, Processes, Materials, Machines, Information, and Resources

3.      Systems Thinking and the Analysis of Integrated Operational Systems

4.      Productivity, Efficiency, Effectiveness, Utilization, and Performance Measurement

5.      Process Mapping, Flowcharts, SIPOC, and Value Stream Mapping

6.      Work Study Principles, Method Study, and Process Improvement

7.      Identifying Waste, Delays, Redundancies, Constraints, and Non-Value-Added Activities

8.      Lean Manufacturing Principles, Kaizen, PDCA, and Continuous Improvement

9.      Case Study: Analyzing Productivity Losses in a Manufacturing Process

10.  Practical Exercise: Developing a Current-State Process Map and Initial Improvement Opportunities

Day 2: Work Measurement, Capacity, Layout, Ergonomics, and Production Systems

Module 2: Work Measurement, Capacity, Layout, Ergonomics, and Production Systems

1.      Time Study Principles, Work Sampling, and Standard Time Development

2.      Performance Rating, Allowances, and Standard Work Measurement

3.      Capacity Planning, Capacity Utilization, and Resource Requirements

4.      Takt Time, Cycle Time, Lead Time, and Production Flow Analysis

5.      Line Balancing, Workstation Design, and Production Flow Optimization

6.      Facility Layout Principles, Material Handling, and Workplace Flow

7.      Ergonomics, Human Factors, Workstation Design, and Occupational Efficiency

8.      Standard Work, Visual Management, 5S, and Workplace Organization

9.      Case Study: Redesigning a Production Line to Improve Throughput

10.  Practical Exercise: Conducting a Time Study and Developing an Improved Workstation or Line-Balancing Plan

Day 3: Quality Engineering, Inventory, Scheduling, and Operational Control

Module 3: Quality Engineering, Inventory, Scheduling, and Operational Control

1.      Industrial Quality Engineering and the Relationship Between Quality and Productivity

2.      Statistical Process Control, Control Charts, and Process Variation

3.      Process Capability, Defect Reduction, and Six Sigma Fundamentals

4.      Root Cause Analysis Using Pareto, Five Whys, and Fishbone Techniques

5.      Failure Mode and Effects Analysis for Process and Product Risk

6.      Inventory Systems, Economic Order Quantity, Safety Stock, and Reorder Points

7.      Production Planning, Scheduling, Sequencing, and Resource Allocation

8.      Bottleneck Analysis, Theory of Constraints, and Throughput Improvement

9.      Case Study: Resolving Quality, Inventory, and Production Scheduling Problems

10.  Practical Simulation: Improving Throughput While Managing Quality and Inventory Constraints

Day 4: Operations Research, Optimization, Cost Engineering, and Advanced Improvement

Module 4: Operations Research, Optimization, Cost Engineering, and Advanced Improvement

1.      Introduction to Operations Research and Quantitative Industrial Engineering

2.      Linear Programming and Resource Allocation Problems

3.      Optimization of Production Mix, Capacity, Materials, and Operational Resources

4.      Queuing Systems, Waiting Times, Service Capacity, and Flow Optimization

5.      Decision Analysis, Scenario Evaluation, and Engineering Trade-Offs

6.      Cost Analysis, Cost of Poor Performance, and Industrial Cost Reduction

7.      Reliability, Maintenance Strategies, and Equipment Performance Improvement

8.      Six Sigma DMAIC, Advanced Problem Solving, and Statistical Improvement Methods

9.      Case Study: Optimizing Resources and Reducing Operating Costs in a Complex Production System

10.  Practical Exercise: Developing an Optimization-Based Improvement Proposal

Day 5: Digital Industrial Engineering, Smart Operations, Sustainability, and Strategic Implementation

Module 5: Digital Industrial Engineering, Smart Operations, Sustainability, and Strategic Implementation

1.      Digital Industrial Engineering and Data-Driven Operational Improvement

2.      Industrial Data Collection, Dashboards, Analytics, and Performance Visualization

3.      Simulation Modeling for Production, Logistics, Capacity, and Process Decisions

4.      Automation, Robotics, IoT, and Industry 4.0 Applications in Industrial Engineering

5.      Predictive Analytics, Artificial Intelligence, and Intelligent Process Optimization

6.      Sustainable Industrial Engineering, Energy Efficiency, Waste Reduction, and Resource Optimization

7.      Resilient Operations, Supply Chain Integration, and Industrial Risk Management

8.      Developing Industrial Engineering KPIs, Improvement Portfolios, and Business Cases

9.      Case Study: Designing a Smart, Lean, and Sustainable Industrial Operation

10.  Capstone Exercise: Developing an Industrial Engineering Improvement Project and Implementation Roadmap